The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE)

Abstract

In pharmacoepidemiology, routinely collected data from electronic health records (including primary care databases, registries, and administrative healthcare claims) are a resource for research evaluating the real world effectiveness and safety of medicines. Currently available guidelines for the reporting of research using non-randomised, routinely collected data—specifically the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) and the Strengthening the Reporting of OBservational studies in Epidemiology (STROBE) statements—do not capture the complexity of pharmacoepidemiological research. We have therefore extended the RECORD statement to include reporting guidelines specific to pharmacoepidemiological research (RECORD-PE). This article includes the RECORD-PE checklist (also available on www.record-statement.org) and explains each checklist item with examples of good reporting. We anticipate that increasing use of the RECORD-PE guidelines by researchers and endorsement and adherence by journal editors will improve the standards of reporting of pharmacoepidemiological research undertaken using routinely collected data. This improved transparency will benefit the research community, patient care, and ultimately improve public health.

Reproducing Protocol‐Based Studies Using Parameterizable Tools—Comparison of Analytic Approaches Used by Two Medical Product Surveillance Networks

“…Findings from the Sentinel and CNODES studies are also available in the public domain. The current practice of Sentinel and CNODES are consistent with the recommendations to improve study reproducibility jointly made by the International Society for Pharmacoeconomics and Outcomes Research and the International Society for Pharmacoepidemiology, as well as with other published standards such as the REporting of studies Conducted using Observational Routinely collected health Data for Pharmacoepidemiology (RECORD‐PE) …”

Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond

“…To maximise the public benefit of health data research, it is imperative that data quality issues or biases within the data are understood and accounted for in the analysis process. There are challenges in using data not originally collected for research; hence, there is a large body of literature focused on assessing the validity of routinely collected health data . This is also now extending to validation of some innovative data sources, such as sensor data .…”

“…There are challenges in using data not originally collected for research; hence, there is a large body of literature focused on assessing the validity of routinely collected health data. 27,28 This is also now extending to validation of some innovative data sources, such as sensor data. 29 However, there is a rapidly growing number of new data sources for which the limitations and potential biases have not been identified.…”

Authors' reply to: “The role of long‐term effects of allopurinol on cardiovascular outcomes and all‐cause mortality in diabetes”

“…We do not agree with Dr Abtahi's assertion that our analysis was susceptible to exposure misclassification , which is incorrect attribution of exposed follow‐up time to unexposed follow‐up time, or vice versa. Rather, Dr Abtahi's concern regards a biological lag , in which the effects of allopurinol might not manifest for a period of time after initiation, or would endure for a period of time following discontinuation . We selected a lag time/grace period of 14 days to address the potential of reverse causality (ie, discontinuation of allopurinol as a marker of impending outcomes).…”